Five soil sample splitting methods (riffle splitting, paper cone riffle splitting, fractional shoveling, coning and quartering, and grab sampling) were evaluated with synthetic samples to verify Pierre Gy sampling theory expectations. Individually prepared samples consisting of layers of sand, NaCl and magnetite were left layered until splitting to simulate stratification from transport or density effects. Riffle splitting performed the best, with approximate 99% confidence levels of less than 2%, followed by paper cone riffle splitting. Coning and quartering and fractional shoveling were associated with significantly higher variability and also took much longer to perform. Common grab sampling was the poorest performer, with approximate 99% confidence levels of 100%±150% and biases of 15%±20%. Method performance rankings were in qualitative agreement with expectations from Gy sampling theory. Precision results depended on the number of increments, the type of increment, and other factors influencing the probability of selecting a particle at random, and were all much higher than Pierre Gy's fundamental error estimate of 1%. A critical factor associated with good performance for these methods is a low conditional probability of sampling adjacent particles. Accuracy levels were dominated by the sampling process rather than by the analytical method. Sampling accuracy was at least two orders of magnitude worse than the accuracy of the analytical method. Published in
Samples from a hazardous waste site contaminated
with lead and cadmium were analyzed by four
independent laboratories, each using a different
technique: atomic absorption spectroscopy (AAS),
X-ray fluorescence (XRF) spectroscopy, inductively
coupled plasma−atomic emission spectroscopy (ICP-AES), and potentiometric stripping analysis (PSA). The
four data sets were retrospectively analyzed to (1)
establish the magnitudes of uncertainty in the measurements, (2) evaluate the comparability of the four
instrumental methods, and (3) determine if any significant
correlations existed between individual sets of data.
In general, the four techniques gave comparable
results for the analysis of lead and cadmium, with the
best agreement between PSA and AAS. Concentrations determined by PSA were higher than those
measured by ICP-AES, AAS, and XRF, while concentrations determined by XRF were lower than or equal
to recoveries determined by ICP-AES and AAS.
Principal
component analysis determined that the two major
principal components in the sample space of the
data set were analyte concentration and sample
preparation. The ICP-AES data were used to look
for correlations among other elements in the samples.
It was shown that concentrations of four of these
elements (aluminum, zinc, iron, and calcium) were
significantly higher than 19 other elements determined by ICP-AES. Principal component analysis on
those 19 elements showed a first-component variation
attributable to an analyte concentration effect and a
second-component variation attributable to an analyst-day effect.
SUMMARYEnvironmental data are usually multivariate, with the variables conforming to some correlation structure.Occasionally, measurements which d o not conform in structure or magnitude may occur in one or more variables. It is important (1) to characterize these discordancies in terms of the disturbed variables and the direction and magnitude of the anomalous error and (2) t o associate each discordant observation with a specific cause of measurement error in order to prevent further mismeasurement. We describe a procedure for identifying suspected causes of discordant observations in otherwise multinormal data sets. Variables are assigned to groups, each of which is associated with a specific cause of measurement error. Discordant observations are identified with the generalized distance test or the multivariate kurtosis test.
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